MRI Brain Image Segmentation Using Interactive Multiobjective Evolutionary Approach

2016 ◽  
pp. 10-29
Author(s):  
Anirban Mukhopadhyay
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithms ◽  
Input Image ◽  
Interactive Approach ◽  
Brain Images ◽  
Robust Clustering ◽  
Evolutionary Clustering ◽  
Mri Brain ◽  
Human Decision ◽  
Validity Measures

The problem of image segmentation is frequently modeled as a problem of clustering the pixels of the images based on their intensity levels. In some recent studies, multiobjective clustering algorithms, where multiple cluster validity measures are optimized simultaneously for yielding robust clustering solutions have been proposed. It has been observed that the same set of validity measures optimized simultaneously do not generally perform well for all image datasets. In view of this, in this article, an interactive approach for multiobjective clustering is proposed for segmentation of multispectral Magnetic Resonance Image (MRI) of the human brain. In this approach, a human decision maker interacts with the multiobjective evolutionary clustering technique during execution in order to obtain the final clustering, the suitable set of validity measures for the input image, as well as the number of clusters by employing a variable-length encoding of the chromosomes. The effectiveness of the proposed method is demonstrated on many simulated normal and MS lesion MRI brain images.

scholarly journals PENERAPAN CITRA TERKOMPRESI PADA SEGMENTASI CITRA MENGGUNAKAN ALGORITMA K-MEANS

2018 ◽  
Vol 2 (1) ◽  
pp. 65-74
Author(s):  
Angga Wijaya Kusuma ◽  
Rossy Lydia Ellyana
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Performance Test ◽  
Input Image ◽  
Magnetic Resonance Imaging Image ◽  
Resonance Imaging ◽  
Compression Process ◽  
Mri Brain ◽  
Mri Image

In the development of an image not only as a documentation of events. One area that requires image processing is in the field of medicine is radiology. In radiology there is a medical image required by doctors and researchers to be processed for patient analysis. One of the important problems in image processing and pattern recognition is image segmentation into homogeneous areas. Segmentation in medical images will result in a medical image with area boundaries that are important information for analysis. This research applies k-means algorithm to MRI (Magnetic Resonance Imaging) image segmentation. The input image used is the image of MRI (brain and breast) has gone through the compression stage. This compression process is done with the aim of reducing memory usage but the critical information content of MRI image is still maintained. The image of the segmentation result is evaluated through performance test using GCE, VOI, MSE, and PSNR parameters.

Automatic MRI Brain Image Segmentation Using Gravitational Search-Based Clustering Technique

2017 ◽  
pp. 115-130
Author(s):  
Vijay Kumar ◽  
Jitender Kumar Chhabra ◽  
Dinesh Kumar
Keyword(s):  
Image Segmentation ◽  
Brain Images ◽  
Brain Image ◽  
Clustering Technique ◽  
Mri Brain ◽  
Gravitational Search ◽  
The Brain ◽  
Mri Brain Image ◽  
Brain Tissues ◽  
Brain Image Segmentation

Image segmentation plays an important role in medical imaging applications. In this chapter, an automatic MRI brain image segmentation framework using gravitational search based clustering technique has been proposed. This framework consists of two stage segmentation procedure. First, non-brain tissues are removed from the brain tissues using modified skull-stripping algorithm. Thereafter, the automatic gravitational search based clustering technique is used to extract the brain tissues from the skull stripped image. The proposed algorithm has been applied on four simulated T1-weighted MRI brain images. Experimental results reveal that proposed algorithm outperforms the existing techniques in terms of the structure similarity measure.

Automatic MRI Brain Image Segmentation Using Gravitational Search-Based Clustering Technique

2014 ◽  
pp. 313-326 ◽  
Author(s):  
Vijay Kumar ◽  
Jitender Kumar Chhabra ◽  
Dinesh Kumar
Keyword(s):  
Image Segmentation ◽  
Brain Images ◽  
Brain Image ◽  
Clustering Technique ◽  
Mri Brain ◽  
Gravitational Search ◽  
The Brain ◽  
Mri Brain Image ◽  
Brain Tissues ◽  
Brain Image Segmentation

Image segmentation plays an important role in medical imaging applications. In this chapter, an automatic MRI brain image segmentation framework using gravitational search based clustering technique has been proposed. This framework consists of two stage segmentation procedure. First, non-brain tissues are removed from the brain tissues using modified skull-stripping algorithm. Thereafter, the automatic gravitational search based clustering technique is used to extract the brain tissues from the skull stripped image. The proposed algorithm has been applied on four simulated T1-weighted MRI brain images. Experimental results reveal that proposed algorithm outperforms the existing techniques in terms of the structure similarity measure.

Image Segmentation Methods and Applications in MRI Brain Images

2015 ◽  
Vol 32 (6) ◽  
pp. 413-427 ◽  
Author(s):  
Sepideh Yazdani ◽  
Rubiyah Yusof ◽  
Alireza Karimian ◽  
Mohsen Pashna ◽  
Amirshahram Hematian
Keyword(s):  
Image Segmentation ◽  
Brain Images ◽  
Mri Brain ◽  
Segmentation Methods ◽  
Mri Brain Images

Simulation of 3D MRI brain images for quantitative evaluation of image segmentation algorithms

2000 ◽  
Author(s):  
Gudrun Wagenknecht ◽  
Hans-Juergen Kaiser ◽  
Thorsten Obladen ◽  
Osama Sabri ◽  
Udalrich Buell
Keyword(s):  
Image Segmentation ◽  
Quantitative Evaluation ◽  
3D Mri ◽  
Brain Images ◽  
Mri Brain ◽  
Segmentation Algorithms ◽  
Mri Brain Images

An Extended Fuzzy C-Means Segmentation for an Efficient BTD With the Region of Interest of SCP

2021 ◽  
Vol 12 (4) ◽  
pp. 11-24
Author(s):  
Subba Reddy K. ◽  
Rajendra Prasad K.
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Region Of Interest ◽  
Tumor Segmentation ◽  
Brain Images ◽  
Brain Image ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Irregular Boundaries ◽  
Mri Brain Images

Magnetic resonance imaging (MRI) is the primary source to diagnose a brain tumor or masses in the medical sciences. It is emerging to detect the tumors from the scanned MRI brain images at early stages for the best treatments. Existing image segmentation techniques, morphological, fuzzy c-means are wildly successful in the extraction region of interest (ROI) in brain image segmentation. Proper extraction of ROIs is useful for regularizing the regions of tumors from the brain image with effective binarization in the segmentation. However, the existing techniques are limiting the irregular boundaries or shapes in tumor segmentation. Thus, this paper presents the proposed work extending the FCM with the spatial correlated pixel (RSCP), known as FCM-RSCP. It overcomes the problem of irregular boundaries by assessing correlated spatial information during segmentation. Benchmarked MRI brain images are used in the experiment for demonstrating the efficiency of the proposed methodology.

scholarly journals Segmentation of Brain Subjects in MR Images using Hybrid Segmentation Technique

2019 ◽  
Vol 9 (1S4) ◽  
pp. 724-728
Keyword(s):  
Image Segmentation ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Input Image ◽  
Vital Role ◽  
Clustering Methods ◽  
Brain Images ◽  
Additional Information ◽  
Fuzzy Clustering Methods ◽  
Relevant Group

Image segmentation takes place a vital role in the area of biomedical applications. Magnetic resonance brain images with and without Alzheimer’s disease have been preferred for the detection and staging the AD. Clustering is one of the extensively implemented image segmentation principle which differentiates group in such a way that samples of the relevant group are related to each other than samples associated to various groups. There has been significant concern recently in the utilization of fuzzy clustering methods, which keep additional information from the input image than the clustering principle. Modified Fuzzy C Means (MFCM) algorithm is extensively preferable because of its flexibility which leads the pixels to exist to various classes with changing the degrees of membership. Cluster initialization process has been done with MFCM and the performance of the segmentation algorithm has enhanced with Binary Gravitational search algorithm. Various brain subjects such as White Matter (WM), Grey matter (GM), hippocampus region, Cerebrospinal Fluid (CSF) are segmented for the detection of AD. The BGSA with MFCM algorithm has achieved better outcomes and it is compared with various existing techniques. The accuracy of the proposed technique is about 93.3%.

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